Perception Machine Learning Engineer - ADAS

- Develop and optimize perception algorithms for autonomous driving systems using camera and LiDAR data.

- Design and implement cutting-edge deep learning algorithms for 2D/3D object detection, segmentation, tracking, and multi-task learning.

- Research and integrate BEV-based transformer models for perception tasks.

- Collaborate with cross-functional teams to ensure seamless integration and robust implementation.

- Test, release, and deploy perception algorithms into Lucid production programs.

- Support validation and verification of perception algorithms using prototype and pre-production vehicles.

- Propose innovative software algorithms to enhance future autonomous driving capabilities.

- Strong theoretical foundations and expertise in deep learning algorithms, including object detection, tracking, and segmentation.

- Proficient in Python with a focus on clean, efficient, and scalable software development.

- Comfortable working with large codebases and debugging complex machine learning models.

- Experience with PyTorch or other ML frameworks (e.g., TensorFlow, MXNet).

- Ability to design and construct evaluation pipelines to unit-test ML models under diverse conditions and environments.

- Bachelor's degree in Computer Engineering, Electrical Engineering, Automotive Engineering, Mechanical Engineering, or a related field.

- Minimum of 3 years of relevant work experience, or a Ph.D. in a related field for a senior position.

- Experience developing BEV transformer models for perception.

- Proficiency in C++ with experience writing efficient, maintainable code.

- Practical, hands-on approach to solving complex problems in autonomous driving.

- Experience in testing and validating perception systems in real-world conditions.

- Expertise in component and system integration, testing, and verification at the system and vehicle levels.